Purpose: The PRECISE score estimates the likelihood of radiological progression in patients on active surveillance (AS) for prostate cancer (PCa) with serial multiparametric magnetic resonance imaging (mpMRI). A PRECISE score of 1 or 2 denotes radiological regression, PRECISE 3 indicates stability and PRECISE 4 or 5 implies progression. We evaluated the inter-reader reproducibility of different apparent diffusion coefficient (ADC) calculations and their relationship to the PRECISE score.
Material And Methods: Baseline and follow-up scans (on the same MR systems) of 30 patients with visible lesions from two different institutions (University College London and Sapienza University of Rome) were analysed by two radiologists (one from each site). The PRECISE score was initially assessed in consensus. At least six weeks later, to reduce the likelihood of being influenced by the consensus PRECISE reading, each radiologist independently calculated ADC for the following: lesion, non-cancerous tissue and urine in the bladder. Normalised ADC ratios were calculated with respect to normal prostatic tissue (npADC) and urine. Spearman's correlation (ρ), intraclass correlation coefficients (ICC), differences in ADC and ROC curves were computed.
Results: Interobserver reproducibility was very good (ρ > 0.8; ICC > 0.90). Lesion ADC (0.91 vs 0.73 × 10 mm/s; p=0.025) and npADC ratio (0.68 vs 0.53; p=0.012) at follow-up mpMRI were different between patients with radiological regression or stability vs progression. Cut-offs of 0.77 × 10 mm/s (lesion ADC) and 0.59 (npADC ratio) could differentiate the two groups (area under the curve: 0.74 and 0.77, respectively).
Conclusion: The ADC, npADC ratio and the PRECISE score should be recorded for MRI-based AS.
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http://dx.doi.org/10.1016/j.mri.2019.12.007 | DOI Listing |
Discov Oncol
January 2025
Department of Thyroid Breast Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Objective: Despite the identification of various prognostic factors for anaplastic thyroid carcinoma (ATC) patients over the years, a precise prognostic tool for these patients is still lacking. This study aimed to develop and validate a prognostic model for predicting survival outcomes for ATC patients using random survival forests (RSF), a machine learning algorithm.
Methods: A total of 1222 ATC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into a training set of 855 patients and a validation set of 367 patients.
Electromagn Biol Med
January 2025
Department of Computer Applications, Kalasalingam Academy of Research and Education - Deemed to be University, Krishnankoil, India.
Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues.
View Article and Find Full Text PDFMol Carcinog
January 2025
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
The standard therapy for locally unresectable advanced non-small cell lung cancer (NSCLC) is comprised of chemoradiotherapy (CRT) before immunotherapy (IO) consolidation. However, how to predict treatment outcomes and recognize patients that will benefit from IO remain unclear. This study aimed to identify prognostic biomarkers by integrating computed tomography (CT)-based radiomics and genomics.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Electrical and Information Engineering, Zhengzhou University, No. 100, Science Avenue, Hightech District, Zhengzhou City 450001, Henan Province, China.
Structural network control principles provided novel and efficient clues for the optimization of personalized drug targets (PDTs) related to state transitions of individual patients. However, most existing methods focus on one subnetwork or module as drug targets through the identification of the minimal set of driver nodes and ignore the state transition capabilities of other modules with different configurations of drug targets [i.e.
View Article and Find Full Text PDFCancer Res Commun
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Eisai.Co.,Ltd., Tsukuba, Ibaraki, Japan.
Combination therapy with anti-angiogenic drugs and immune checkpoint inhibitors has shown enhanced clinical activity and has been approved for the treatment of multiple tumor types. Despite extensive research, predictive biomarkers for combination therapy remain poorly understood. Microvessel density (MVD), a surrogate marker for aberrant angiogenesis measured by immunohistochemistry (IHC), has been associated with response to monotherapy with anti-angiogenesis inhibitors.
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